ابعاد، تکامل، اثرات و چالش های کلان داده
Abstract
1- The day of big data
2- Dimensions of big data
3- An integrated view of big data
4- Evolution of big data and data analytics
5- An illustrative example: Big data analysis of merchant reviews
6- Impacts of big data
7- Challenges in big data
8- The future of big data
References
Abstract
Big data represents a new technology paradigm for data that are generated at high velocity and high volume, and with high variety. Big data is envisioned as a game changer capable of revolutionizing the way businesses operate in many industries. This article introduces an integrated view of big data, traces the evolution of big data over the past 20 years, and discusses data analytics essential for processing various structured and unstructured data. This article illustrates the application of data analytics using merchant review data. The impacts of big data on key business performances are then evaluated. Finally, six technical and managerial challenges are discussed.
The day of big data
The emerging technological development of big data is recognized as one of the most important areas of future information technology and is evolving at a rapid speed, driven in part by social media and the Internet of Things (IoT) phenomenon. The technological developments in big data infrastructure, analytics, and services allow firms to transform themselves into data-driven organizations. Due to the potential of big data becoming a game changer, every firm needs to build capabilities to leverage big data in order to stay competitive. IDC (2015) forecasted that the big data technology and services market will grow at a compound annual growth rate of 23.1% over the 2014—2019 period, with annual spending reaching $48.6 billion in 2019. While structured data is an essential part of big data, more and more data are created in unstructured video and image forms, which traditional data management technologies are inadequate to process. A large portion of data worldwide have been generated by billions of IoT devices such as smart home appliances, wearable devices, and environmental sensors. Gartner (2015) forecasted that 4.9 billion connected objects would be in use in 2015–—up 30% from 2014–—and will reach 25 billion by 2020. To meet the ever-increasing storage and processing needs of big data, new big data platforms are emerging, including NoSQL1 databases as an alternative to traditional relational databases and Hadoop as an open source framework forinexpensive distributed clusters of commodity hardware. In this article, I start with a discussion of big data dimensions and trace the evolution of big data since 1995. Then, I illustrate the application of data analytics using a scenario involving merchant review data. In the following section, I discuss impacts of big data on various business performances. Finally, I discuss six technical and managerial challenges: data quality, data security, privacy, data management, investment justification, and shortage of qualified data scientists.